Prediction of telecommunications market behavior based on LSTM models
Vol 8, Issue 15, 2024
VIEWS - 839 (Abstract)
Abstract
The telecommunications services market faces essential challenges in an increasingly flexible and customer-adaptable environment. Research has highlighted that the monopolization of the spectrum by one operator reduces competition and negatively impacts users and the general dynamics of the sector. This article aims to present a proposal to predict the number of users, the level of traffic, and the operators’ income in the telecommunications market using artificial intelligence. Deep Learning (DL) is implemented through a Long-Short Term Memory (LSTM) as a prediction technique. The database used corresponds to the users, revenues, and traffic of 15 network operators obtained from the Communications Regulation Commission of the Republic of Colombia. The ability of LSTMs to handle temporal sequences, long-term dependencies, adaptability to changes, and complex data management makes them an excellent strategy for predicting and forecasting the telecom market. Various works involve LSTM and telecommunications. However, many questions remain in prediction. Various strategies can be proposed, and continued research should focus on providing cognitive engines to address further challenges. MATLAB is used for the design and subsequent implementation. The low Root Mean Squared Error (RMSE) values and the acceptable levels of Mean Absolute Percentage Error (MAPE), especially in an environment characterized by high variability in the number of users, support the conclusion that the implemented model exhibits excellent performance in terms of precision in the prediction process in both open-loop and closed-loop.
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- Abdoli, G. (2020). Comparing the Prediction Accuracy of LSTM and ARIMA Models for Time-Series with Permanent Fluctuation. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.3612487
- Adetunji, A. J., & Moses, B. O. (2022). The Role of Network Technologies in the Enhancement of the Health, Education, and Energy Sectors. Network and Communication Technologies, 7(1), 39. https://doi.org/10.5539/nct.v7n1p39
- Agha, A. A., Rashid, A., Rasheed, R., et al. (2021). Antecedents of customer loyalty at telecom sector. Turkish Online Journal of Qualitative Inquiry, 12(9).
- Almaghrabi, H., Li, A., & Soh, B. (2022). IiCE: A Proposed System Based on IoTaaS to Study Administrative Efficiency in Primary Schools. In: IoT as a Service. Springer International Publishing. pp. 121–138.
- Almaghrabi, H., Soh, B., & Li, A. (2024). Using ML to Predict User Satisfaction with ICT Technology for Educational Institution Administration. Information, 15(4), 218. https://doi.org/10.3390/info15040218
- Al-Sharafi, M. A., Al-Emran M., Tan, G. W.-H., & Ooi, K.-B. (2024). Current and Future Trends on Intelligent Technology Adoption. Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-61463-7
- Alzheev, A. V., & Kochkarov, R. A. (2020). Comparative Analysis of ARIMA and lSTM Predictive Models: Evidence from Russian Stocks. Finance: Theory and Practice, 24(1), 14–23. https://doi.org/10.26794/2587-5671-2020-24-1-14-23
- Bardey, D., Sáenz, B., Aristizábal, D., & Gómez, S. (2020). Impact of the concentration of the mobile market in Colombia on Competitiveness (Spanish). Available online: https://economia.uniandes.edu.co/sites/default/files/eventos/Estudio-movistar.pdf (accessed on 3 May 2024).
- Beckert, W., & Siciliani, P. (2022). Protecting Sticky Consumers in Essential Markets. Review of Industrial Organization, 61(3), 247–278. https://doi.org/10.1007/s11151-022-09880-z
- Bensalah, F., Bahnasse, A., & El Hamzaoui, M. (2019). Quality of Service Performance Evaluation of Next-Generation Network. In: Proceedings of the 2019 2nd International Conference on Computer Applications & Information Security (ICCAIS). https://doi.org/10.1109/cais.2019.8769576
- Berradi, Z., Lazaar, M., Mahboub, O., et al. (2021). A Comprehensive Review of Artificial Intelligence Techniques in Financial Market. In: Proceedings of the 2020 6th IEEE Congress on Information Science and Technology (CiSt). https://doi.org/10.1109/cist49399.2021.9357175
- Berret, B., & Jean, F. (2020). Efficient computation of optimal open-loop controls for stochastic systems. Automatica, 115, 108874. https://doi.org/10.1016/j.automatica.2020.108874
- Comisión de Regulación de Comunicaciones. (2024). Postscript - Beyond the data (Spanish). Available online: https://postdata.gov.co/search/type/dataset (accessed on 1 July 2024).
- Czaplewski, M., & Zakrzewska, M. (2023). Frequency Spectrum Management within the Regulatory Framework of the European Union’s Telecommunications Market. European research studies journal, xxvi, (4), 382–390. https://doi.org/10.35808/ersj/3291
- Ding, X., Lv, Q., Zou, Y., et al. (2022). Spectrum Prediction for Satellite based Spectrum-Sensing Systems Using Deep Learning. IEEE. https://doi.org/10.1109/globecom48099.2022.10000832
- Doğan, E. (2021). Performance analysis of LSTM model with multi-step ahead strategies for a short-term traffic flow prediction. Scientific Journal of Silesian University of Technology. Series Transport, 111, 15–31. https://doi.org/10.20858/sjsutst.2021.111.2
- Eshbayev, O., Rakhimova, S., Mirzaliev, S., et al. (2022). A systematic mapping study of effective regulations and policies against digital monopolies: visualizing the recent status of anti-monopoly research areas in the digital economy. In: Proceedings of the 6th International Conference on Future Networks & Distributed Systems. https://doi.org/10.1145/3584202.3584205
- Fander, A., & Yaghoubi, S. (2022). Dynamic and stochastic modeling for a closed-loop automotive supply chain under fuel issue and government intervention: A case study. Computers & Industrial Engineering, 174, 108765. https://doi.org/10.1016/j.cie.2022.108765
- Fisher, M., Gallino, S., & Li, J. (2018). Competition-Based Dynamic Pricing in Online Retailing: A Methodology Validated with Field Experiments. Management Science, 64(6), 2496–2514. https://doi.org/10.1287/mnsc.2017.2753
- Gao, N., Qu, L.-C., & Jiang, Y.-T. (2022). A System Dynamic Model of Closed-Loop Supply Chain considering Recovery Strengthening and Product Differentiation. Mathematical Problems in Engineering, 2022, 1–15. https://doi.org/10.1155/2022/3778597
- Giral, D., Hernández, C., & Salgado, C. (2021). Spectral decision in cognitive radio networks based on deep learning. Expert Systems with Applications, 180, 115080. https://doi.org/10.1016/j.eswa.2021.115080
- Giral-Ramírez, D. (2022). Intelligent Fault Location Algorithms for Distributed Generation Distribution Networks: A Review. Przegląd Elektrotechniczny, 1(7), 139–146. https://doi.org/10.15199/48.2022.07.23
- Glass, V., & Tardiff, T. (2023). Analyzing Competition in the Online Economy. The Antitrust Bulletin, 68(2), 167–190. https://doi.org/10.1177/0003603x231163001
- Hajar, M. A., Al-Sharafi, M. A., Ibrahim, N., et al. (2024) Innovation Practices and Challenges in the Telecommunication Industry: A Roadmap for Value Creation and Sustainable Growth BT. In: Current and Future Trends on Intelligent Technology Adoption. Springer Nature Switzerland. Springer. pp. 137–170. https://doi.org/10.1007/978-3-031-61463-7_8
- Hussain, W., & Jan, M. A. (editors). (2022). IoT as a Service. Springer International Publishing. https://doi.org/10.1007/978-3-030-95987-6
- Jung, J., & Katz, R. (2022). Spectrum flexibility and mobile telecommunications development. Utilities Policy, 75, 101351. https://doi.org/10.1016/j.jup.2022.101351
- Latif, R. M. A., Naeem, M. R., Rizwan, O., et al. (2021). A Smart Technique to Forecast Karachi Stock Market Share-Values using ARIMA Model. In: Proceedings of the 2021 International Conference on Frontiers of Information Technology (FIT). https://doi.org/10.1109/fit53504.2021.00065
- Lu, X.-Q., Tian, J., Liao, Q., et al. (2024). CNN-LSTM based incremental attention mechanism enabled phase-space reconstruction for chaotic time series prediction. Journal of Electronic Science and Technology, 22(2), 100256. https://doi.org/10.1016/j.jnlest.2024.100256
- Myers, J. H., & Tauber, E. (2011). Market structure analysis. Marketing Classics Press.
- Nguyen Chau, T., Vu Thi Hong, N., Pham Thi Thu, T., et al. (2024). Re-examining the effects of information and communication technology on economic growth. Technology in Society, 78, 102646. https://doi.org/10.1016/j.techsoc.2024.102646
- Organización para la Cooperación y el Desarrollo Económicos (OCDE). (2020). Economy Profile of Colombia - Doing Business 2020 Indicators. Available online: https://www.doingbusiness.org/content/dam/doingBusiness/country/c/colombia/COL.pdf (accessed on 2 March 2024).
- Petropoulos, F., Apiletti, D., Assimakopoulos, V., et al. (2022). Forecasting: theory and practice. International Journal of Forecasting, 38(3), 705–871. https://doi.org/10.1016/j.ijforecast.2021.11.001
- Poobalan, A., Ganapriya, K., Kalaivani, K., et al. (2025). A novel and secured email classification using deep neural network with bidirectional long short-term memory. Computer Speech & Language, 89, 101667. https://doi.org/10.1016/j.csl.2024.101667
- Qazi, A., & Al-Mhdawi, M. K. S. (2024). Exploring critical drivers of global innovation: A Bayesian Network perspective. Knowledge-Based Systems, 299, 112127. https://doi.org/10.1016/j.knosys.2024.112127
- Quinn, J., McEachen, J., Fullan, M., et al. (2019). Dive into deep learning: Tools for engagement. Corwin Press.
- Sheth, J., Jain, V., & Ambika, A. (2020). Repositioning the customer support services: the next frontier of competitive advantage. European Journal of Marketing, 54(7), 1787–1804. https://doi.org/10.1108/ejm-02-2020-0086
- Tiwari, A., Chugh, A., & Sharma, A. (2023). Uses of artificial intelligence with human-computer interaction in psychology. Innovations in Artificial Intelligence and Human-Computer Interaction in the Digital Era, 173–205. https://doi.org/10.1016/b978-0-323-99891-8.00003-6
- Wang, X., Peng, T., Zuo, P., et al. (2020). Spectrum Prediction Method for ISM Bands Based on LSTM. In: Proceedings of the 2020 5th International Conference on Computer and Communication Systems (ICCCS). https://doi.org/10.1109/icccs49078.2020.9118535
- Wang, Y., Zhou, Y., Yan, K., et al. (2023). Software Usage Prediction Based on Hybrid LSTM-ARIMA Algorithm. In: Proceedings of the 2023 IEEE 5th International Conference on Power, Intelligent Computing and Systems (ICPICS). https://doi.org/10.1109/icpics58376.2023.10235473
- Wen, X., Liao, J., Niu, Q., et al. (2024). Deep learning-driven hybrid model for short-term load forecasting and smart grid information management. Scientific Reports, 14(1). https://doi.org/10.1038/s41598-024-63262-x
- World Bank Group. (2020). Economy Profile of Colombia - Doing Business 2020 Indicators. Available online: https://www.doingbusiness.org/content/dam/doingBusiness/country/c/colombia/COL.pdf (accessed on 3 May 2024).
DOI: https://doi.org/10.24294/jipd8226
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